Slide 1

Bioinformatics vs statistics vs computational biology

Things bioinformaticians care about

• Experimental design
• The biological question
• Statistics
• Reproducibility
• File formats

How many samples are sufficient?

• Depends on the question
• Depends on the technology
• Depends on the variability

How many samples are sufficient?

The bottom line

Talk to your bioinformatician early!

Statistics matters

What is a p-value?

\(H_0\): The null hypothesis, no effect

\(H_1\): The alternative hypothesis, there is an effect

We run a test, we get a p-value. What is it?

• Probability that \(H_0\) is true, given the data
• Probability that \(H_1\) is wrong, given the data
• Probability that the data is random

Our intuition is bayesian, not frequentist

Frequentist Statistics	Bayesian Statistics
1. Probability is defined as the long-run frequency of events	1. Probability represents a degree of belief or certainty about an event
2. Parameters (like the “true value”) are fixed but unknown quantities.	2. Parameters are treated as random variables with their own probability distributions.
3. Asking about the probability of a hypothesis does not make sense	3. Asking about the probability of a hypothesis is the main goal

Going beyond the p-value

• Confidence intervals
• Effect sizes
• Power analysis

Why is that important?

P-values are the language of science, whether we like them (we don’t) or not.

• Use effect sizes always
• Never rely on p-values alone

Tale of two papers

Tale of two papers

Tale of two papers

Tale of two papers

Tale of two papers

Lessons learned

• A lot depends on how you analyze your data

Reproducible workflows with Rmarkdown

flowchart LR
    A(Program + Text) -->|knitr| B(Text with\nanalysis results)
    B --> C[LaTeX]
    C --> CC[PDF]
    B --> D[Word]
    B --> E[HTML]
    B --> F[Presentation]
    B --> G[Book]

This can be Rmarkdown, Quarto, Jupyter… the goal is that your code and your text are in one place, and the results of your calculations are entered automatically into the text.

Reproducible workflows with Rmarkdown

In systems such ar R markdown, you can put directly your analysis results in your text. For example, when I write that the \(p\)-value is equal to 0.05, I am writing this:

In systems such ar R markdown, you can put directly your
analysis results in your text. For example, when I write that the
$p$-value is equal to `​r p`, I am writing this:

The \(p\)-value above is not entered manually (as 0.05), but is the result of a statistical computation. If the data changes, if your analysis changes, the \(p\)-value above will automatically change as well.

How we work

flowchart LR
    A(Excel) --> B(Data import)
    AA(CSV, TSV) --> B(Data import)
    AAA(fastq, ...) --> B(Data import)
    B --> C[Data\ncleanup]
    C --> D[Long term storage]
    C --> E[Analysis]
    E --> D
    E --> F(Figures)
    E --> G(Manuscript\nfragments)
    E --> H(Tables\nExcel files)
    F --> I[You]
    G --> I
    H --> I
    I --> E

In the diagram above, two things take usually the most hands-on time:

• Data cleanup
• Fine-tuning the analysis results

Excel and gene names

Excel and gene names

How (not to) work with Excel

Three reasons why you should follow these rules:

1. Fewer chances of errors
2. You bioinformaticians will love you
3. The analysis will be done much faster

How (not to) work with Excel

How (not to) work with Excel

How (not to) work with Excel

How (not to) work with Excel

How (not to) work with Excel

(for your reference)

• Avoid manually changing Excel files
• Never use formatting for data
• Don’t combine values and comments
• Don’t put meta-information into column names
• One sheet = one table
• Header = one line
• Do not use merged cells
• Use consistent file names
• Avoid spaces in file and column names (use underscores)

Things that you should probably learn

• Learn how to code (preferably R or Python)
• Learn reproducible workflows with Rmarkdown or Jupyter

Slide 1

To compile, type quarto render template.qmd

Make sure you have Quarto 1.2 installed from here.

Multicolumn slide

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Incremental list

Incremental contents

First part

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Transitions

Define them in the YAML header or like here, in the slide title.

Types: none, fade, slide, convex, concave, zoom

Code

plot(1:10)

Code

There are many customization options for the code. For example, you can highlight (and even animate) certain lines of code:

a <- rnorm(10)
b <- rnorm(10) + a
c <- a + b * rnorm(10)

You can also specify where precisely should the output of the code go: below the code (default), on the next slide, on a right-hand column…